{
  "edition": "Inside LFM3 · First edition",
  "schema": "lfm3-report/1",
  "models": {
    "LiquidAI/LFM3-1.7B": "40ebe0aa6662c9d40389c2f5d565b6a2c6aebacd",
    "LiquidAI/LFM3-1.7B-Base": "545fc8f4c7b5fa9f405105274cd29c4f8f44b526",
    "LiquidAI/LFM3-VL-2B": "f83d2b0a2296aaa277f394990a9b8b949ddf9691",
    "LiquidAI/LFM3-Audio-2B": "9d49701c372d6011d900f6abb40c6b296fb706dd",
    "LiquidAI/LFM3-Omni-2B": "ce2458cbce4ad970f92032ed94e3d18d30b04f9c",
    "LiquidAI/d1-2B": "97c57047d5852d77e02dfac211f8768aff77a8b2",
    "LiquidAI/d1-omni-2B": "4cccbfd5ba3405ee0f973134bd03e0d9d60ef1d8"
  },
  "figures": {
    "architecture": {
      "file": "architecture.svg",
      "sha256": "7091f6f14a924708c1da963acd300eb655a86e6bcb41405214edc1cd67953738",
      "alt": "Thirteen short-convolution layers alternate with five sliding and six global attention layers. Convolution-only reach is 78 later positions; with sliding attention it is 1,353. Global attention reaches the end of the tested sequence.",
      "values": {
        "layers": [
          "C",
          "C",
          "G",
          "C",
          "S",
          "C",
          "G",
          "C",
          "S",
          "C",
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          "C",
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          "C",
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          "C",
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          "C",
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          "C",
          "S",
          "C",
          "G",
          "C"
        ],
        "conv_reach": 78,
        "sliding_reach": 1353
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      "sources": [
        "results/02_attention_off/instruct.json"
      ],
      "encoding": "Equal layer boxes, type labels. Geometry is schematic, not distance to scale.",
      "minimum_width_px": 640,
      "table": "<details class=\"figure-data\"><summary>Text alternative and data</summary><div class=\"table-scroll\" tabindex=\"0\" role=\"region\" aria-label=\"Supporting figure data\"><table><caption>Layer order, zero-based</caption><thead><tr><th scope=\"col\">Layer</th><th scope=\"col\">Operator</th></tr></thead><tbody><tr><th scope=\"row\">0</th><td>C</td></tr><tr><th scope=\"row\">1</th><td>C</td></tr><tr><th scope=\"row\">2</th><td>G</td></tr><tr><th scope=\"row\">3</th><td>C</td></tr><tr><th scope=\"row\">4</th><td>S</td></tr><tr><th scope=\"row\">5</th><td>C</td></tr><tr><th scope=\"row\">6</th><td>G</td></tr><tr><th scope=\"row\">7</th><td>C</td></tr><tr><th scope=\"row\">8</th><td>S</td></tr><tr><th scope=\"row\">9</th><td>C</td></tr><tr><th scope=\"row\">10</th><td>G</td></tr><tr><th scope=\"row\">11</th><td>C</td></tr><tr><th scope=\"row\">12</th><td>S</td></tr><tr><th scope=\"row\">13</th><td>C</td></tr><tr><th scope=\"row\">14</th><td>G</td></tr><tr><th scope=\"row\">15</th><td>C</td></tr><tr><th scope=\"row\">16</th><td>S</td></tr><tr><th scope=\"row\">17</th><td>C</td></tr><tr><th scope=\"row\">18</th><td>G</td></tr><tr><th scope=\"row\">19</th><td>C</td></tr><tr><th scope=\"row\">20</th><td>S</td></tr><tr><th scope=\"row\">21</th><td>C</td></tr><tr><th scope=\"row\">22</th><td>G</td></tr><tr><th scope=\"row\">23</th><td>C</td></tr></tbody></table></div></details>"
    },
    "heads": {
      "file": "heads.svg",
      "sha256": "e28fee00b6a724d84582b7b03cc26f6a4dea83097bde893c2650bd2a7e1a1012",
      "alt": "Copying falls from 100% to 30% with layer 14 head 5 off. Distant secret-word recall falls from 100% to 13.3% with three nominated recall heads off. These are different tasks.",
      "values": {
        "Copy: clean": 1.0,
        "Copy: copying head off": 0.3,
        "Distant recall: clean": 1.0,
        "Distant recall: top three off": 0.13333333333333333,
        "copy_with_recall_heads_off": 0.975,
        "answer_attention_copy": 0.9330078125,
        "far_n": 30
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      "sources": [
        "results/03_memory_heads/instruct.json"
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      "encoding": "Bar length encodes the value from zero; the labelled axis is shared by every row.",
      "minimum_width_px": 640,
      "table": "<details class=\"figure-data\"><summary>Text alternative and data</summary><div class=\"table-scroll\" tabindex=\"0\" role=\"region\" aria-label=\"Supporting figure data\"><table><caption>Copying versus recall</caption><thead><tr><th scope=\"col\">Condition</th><th scope=\"col\">Value</th></tr></thead><tbody><tr><th scope=\"row\">Copy: clean</th><td>100.0%</td></tr><tr><th scope=\"row\">Copy: copying head off</th><td>30.0%</td></tr><tr><th scope=\"row\">Distant recall: clean</th><td>100.0%</td></tr><tr><th scope=\"row\">Distant recall: top three off</th><td>13.3%</td></tr></tbody></table></div></details>"
    },
    "refusal": {
      "file": "refusal.svg",
      "sha256": "67a4cfacc4326cadb7b2242727ae6cfc2b22b770025b22e634d256d0b0df7786",
      "alt": "Original refusals: 95/100. Modified refusals: 16/94 uncapped harmful replies. Judged recognition: 97/100 original and 98/100 modified. Six modified replies were capped. Recognition is model-judged, not human validation.",
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        "original": {
          "refusals": 95,
          "uncapped": 100,
          "harm_recognition": 97,
          "n": 100,
          "recognized_and_delivered": 2
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        "ablated": {
          "refusals": 16,
          "uncapped": 94,
          "harm_recognition": 98,
          "n": 100,
          "recognized_and_delivered": 48
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      "sources": [
        "results/08_knows_but_complies/judged.json"
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      "encoding": "Lengths encode separate proportions from zero; denominators printed.",
      "minimum_width_px": 640,
      "table": "<details class=\"figure-data\"><summary>Text alternative and data</summary><div class=\"table-scroll\" tabindex=\"0\" role=\"region\" aria-label=\"Supporting figure data\"><table><caption>Refusal and judged harm recognition</caption><thead><tr><th scope=\"col\">Model</th><th scope=\"col\">Refusal</th><th scope=\"col\">Judged recognition</th></tr></thead><tbody><tr><th scope=\"row\">original</th><td>95/100</td><td>97/100</td></tr><tr><th scope=\"row\">ablated</th><td>16/94</td><td>98/100</td></tr></tbody></table></div></details>"
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    "family": {
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      "sha256": "581c88a831461be9dc76f34c060992395006952b2ebb6462df3716cb61b4af9f",
      "alt": "d1 changes are fit by 70.3% vision-language plus 29.7% instruction-model changes, with about 0.5% squared change unexplained. Omni has a fitted Audio coefficient 0.988 with about 17% unexplained. This diagram does not establish exact training history.",
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      "encoding": "Schematic regression relationships. Percent labels are fitted coefficients, not literal training ingredients.",
      "minimum_width_px": 640,
      "table": "<details class=\"figure-data\"><summary>Text alternative and data</summary><div class=\"table-scroll\" tabindex=\"0\" role=\"region\" aria-label=\"Supporting figure data\"><table><caption>Fits to backbone changes from Base</caption><thead><tr><th scope=\"col\">Fit</th><th scope=\"col\">Coefficient</th><th scope=\"col\">Unexplained squared change</th></tr></thead><tbody><tr><th scope=\"row\">d1 ← VL + instruction</th><td>0.703 / 0.297</td><td>0.5%</td></tr><tr><th scope=\"row\">Omni ← Audio</th><td>0.988</td><td>16.8%</td></tr></tbody></table></div></details>"
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      "alt": "Five-task mean accuracy rises from 69.3% for the blend to 80.8% with a rank-one-per-matrix patch, 85.3% at rank eight, and 88.2% at rank 64. d1 reaches 88.6%. Scores are inside d1’s two-way-attention decision harness.",
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      "encoding": "Bar length encodes the value from zero; the labelled axis is shared by every row.",
      "minimum_width_px": 640,
      "table": "<details class=\"figure-data\"><summary>Text alternative and data</summary><div class=\"table-scroll\" tabindex=\"0\" role=\"region\" aria-label=\"Supporting figure data\"><table><caption>Patch size and decision accuracy</caption><thead><tr><th scope=\"col\">Condition</th><th scope=\"col\">Value</th></tr></thead><tbody><tr><th scope=\"row\">Blend · no patch</th><td>69.3%</td></tr><tr><th scope=\"row\">Rank 1 · about 4 MB</th><td>80.8%</td></tr><tr><th scope=\"row\">Rank 8 · about 20 MB</th><td>85.3%</td></tr><tr><th scope=\"row\">Rank 64 · about 147 MB</th><td>88.2%</td></tr><tr><th scope=\"row\">d1 · full residual</th><td>88.6%</td></tr></tbody></table></div></details>"
    },
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      "file": "handles.svg",
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      "alt": "Thinking disabled: no clean replies start thinking. A final-position pulse to layer 6 head 2 reopens thinking on twelve of sixteen prompts. A matched head reopens none. This does not demonstrate improved reasoning.",
      "values": {
        "Clean": 0,
        "Single-head boundary pulse": 0.75,
        "Matched-head control": 0,
        "counts": {
          "clean": 0,
          "L6h2_d+2_boundary": 12,
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            "final": "flute"
          },
          "L6h6_d+2_answer": {
            "tokens": 2,
            "final": "Flute"
          }
        }
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        "results/10b_response_mode/pilot_mps/checkpoints/fresh_arithmetic_0_1__think0__clean.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_arithmetic_1_0__think0__clean.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_arithmetic_1_1__think0__clean.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_extraction_0_0__think0__clean.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_extraction_0_1__think0__clean.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_extraction_1_0__think0__clean.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_extraction_1_1__think0__clean.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_missing_evidence_0_0__think0__clean.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_missing_evidence_0_1__think0__clean.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_missing_evidence_1_0__think0__clean.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_missing_evidence_1_1__think0__clean.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_serialization_0_0__think0__clean.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_serialization_0_1__think0__clean.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_serialization_1_0__think0__clean.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_serialization_1_1__think0__clean.json",
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        "results/10b_response_mode/pilot_mps/checkpoints/fresh_arithmetic_0_1__think0__L6h2_d+2_boundary.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_arithmetic_1_0__think0__L6h2_d+2_boundary.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_arithmetic_1_1__think0__L6h2_d+2_boundary.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_extraction_0_0__think0__L6h2_d+2_boundary.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_extraction_0_1__think0__L6h2_d+2_boundary.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_extraction_1_0__think0__L6h2_d+2_boundary.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_extraction_1_1__think0__L6h2_d+2_boundary.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_missing_evidence_0_0__think0__L6h2_d+2_boundary.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_missing_evidence_0_1__think0__L6h2_d+2_boundary.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_missing_evidence_1_0__think0__L6h2_d+2_boundary.json",
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        "results/10b_response_mode/pilot_mps/checkpoints/fresh_serialization_0_1__think0__L6h2_d+2_boundary.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_serialization_1_0__think0__L6h2_d+2_boundary.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_serialization_1_1__think0__L6h2_d+2_boundary.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_arithmetic_0_0__think0__L6h6_d+2_answer.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_arithmetic_0_1__think0__L6h6_d+2_answer.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_arithmetic_1_0__think0__L6h6_d+2_answer.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_arithmetic_1_1__think0__L6h6_d+2_answer.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_extraction_0_0__think0__L6h6_d+2_answer.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_extraction_0_1__think0__L6h6_d+2_answer.json",
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        "results/10b_response_mode/pilot_mps/checkpoints/fresh_extraction_1_1__think0__L6h6_d+2_answer.json",
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        "results/10b_response_mode/pilot_mps/checkpoints/fresh_missing_evidence_0_1__think0__L6h6_d+2_answer.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_missing_evidence_1_0__think0__L6h6_d+2_answer.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_missing_evidence_1_1__think0__L6h6_d+2_answer.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_serialization_0_0__think0__L6h6_d+2_answer.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_serialization_0_1__think0__L6h6_d+2_answer.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_serialization_1_0__think0__L6h6_d+2_answer.json",
        "results/10b_response_mode/pilot_mps/checkpoints/fresh_serialization_1_1__think0__L6h6_d+2_answer.json"
      ],
      "encoding": "Bar length encodes the value from zero; the labelled axis is shared by every row.",
      "minimum_width_px": 640,
      "table": "<details class=\"figure-data\"><summary>Text alternative and data</summary><div class=\"table-scroll\" tabindex=\"0\" role=\"region\" aria-label=\"Supporting figure data\"><table><caption>New thinking blocks after a boundary pulse</caption><thead><tr><th scope=\"col\">Condition</th><th scope=\"col\">Value</th></tr></thead><tbody><tr><th scope=\"row\">Clean</th><td>0.0%</td></tr><tr><th scope=\"row\">Single-head boundary pulse</th><td>75.0%</td></tr><tr><th scope=\"row\">Matched-head control</th><td>0.0%</td></tr></tbody></table></div></details>"
    },
    "quant": {
      "file": "quant.svg",
      "sha256": "f2d79531f8de7f48318f0a37101704993bf1e87125491454cfc7eb08e4e3af21",
      "alt": "Selected saved three-bit and four-bit formats. The rank-eight activation-weighted repair reduces three-bit divergence by 22% for about 16 MB. At 779 MB, four-bit group-128 beats every tested three-bit format within that storage budget. Storage excludes embeddings and other weights.",
      "values": {
        "variants": {
          "q3_g128": {
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            "KL": 0.6692035900538994,
            "perplexity": 26.20985984802246
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          "q3": {
            "MB": 641.492992,
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            "perplexity": 21.911958694458008
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          "q3+act8": {
            "MB": 657.961728,
            "KL": 0.3802343218514089,
            "perplexity": 19.909198760986328
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          "q3+attn_all8+act32": {
            "MB": 768.858112,
            "KL": 0.2525948327404147,
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          "q4_g128": {
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          "q2": {
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          "bf16": {
            "MB": 2932.539392,
            "KL": 0.0,
            "perplexity": 14.9765625
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        },
        "rank8_recovery": 0.22373988250310417,
        "eligible_three_bit_comparisons": 20
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      "sources": [
        "results/13_quant_repair/full/results.json"
      ],
      "encoding": "Point position encodes decoder matrix MB and KL divergence on linear scales. All budget-eligible three-bit variants are checked, only six are drawn.",
      "minimum_width_px": 640,
      "table": "<details class=\"figure-data\"><summary>Text alternative and data</summary><div class=\"table-scroll\" tabindex=\"0\" role=\"region\" aria-label=\"Supporting figure data\"><table><caption>Selected quantization formats</caption><thead><tr><th scope=\"col\">Format</th><th scope=\"col\">Decoder MB</th><th scope=\"col\">KL divergence</th></tr></thead><tbody><tr><th scope=\"row\">q3_g128</th><td>595.7</td><td>0.669</td></tr><tr><th scope=\"row\">q3</th><td>641.5</td><td>0.490</td></tr><tr><th scope=\"row\">q3+act8</th><td>658.0</td><td>0.380</td></tr><tr><th scope=\"row\">q3+attn_all8+act32</th><td>768.9</td><td>0.253</td></tr><tr><th scope=\"row\">q4_g128</th><td>779.0</td><td>0.171</td></tr><tr><th scope=\"row\">q4</th><td>824.8</td><td>0.114</td></tr></tbody></table></div></details>"
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    "cache": {
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      "sha256": "9d9ab19e1fdb639a02c8231d57a997eb430101bcc662ff8d74af107c14edc98c",
      "alt": "Unchanged prompt: The training improved staff productivity as a. Clean predicts result with p(result) 56.9%. Replacing one final-convolution history value predicts whole, p(result) 12.4%. Restoring the value restores result, p(result) 56.9%. This replays saved output, not live inference.",
      "values": {
        "prompt": "The training improved staff productivity as a",
        "edited_coordinate": 1510,
        "rows": [
          {
            "arm": "clean",
            "word": "result",
            "p_result": 0.5690356492996216
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          {
            "arm": "source_cue",
            "word": "whole",
            "p_result": 0.12379781156778336
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          {
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      "sources": [
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      "encoding": "Cache slots schematic, not scalar magnitudes. Word and p(result) read directly from saved first-token records.",
      "minimum_width_px": 640,
      "table": "<details class=\"figure-data\"><summary>Text alternative and data</summary><div class=\"table-scroll\" tabindex=\"0\" role=\"region\" aria-label=\"Supporting figure data\"><table><caption>The training improved staff productivity as a</caption><thead><tr><th scope=\"col\">Condition</th><th scope=\"col\">Next word</th><th scope=\"col\">p(result)</th></tr></thead><tbody><tr><th scope=\"row\">clean</th><td>result</td><td>56.9%</td></tr><tr><th scope=\"row\">source_cue</th><td>whole</td><td>12.4%</td></tr><tr><th scope=\"row\">restored</th><td>result</td><td>56.9%</td></tr></tbody></table></div></details>"
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      "file": "palette.svg",
      "sha256": "6e3649b6867d54cd1ae087ba2591072a012f33a013eb7aef871d0eea1ce72e5a",
      "alt": "One thousand answers per digit. Black: 0 24.9%, 8 24.2%. Purple: 7 21.8%, 9 26.9%. Square area encodes the share on the same scale across the grid. Other and unparsed answers remain in the denominator, but are not drawn. This is color-word association, not evidence of experience.",
      "values": {
        "n_runs": 1000,
        "answers": 10000,
        "counts": {
          "0": {
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            "black": 249,
            "brown": 153,
            "yellow": 139,
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          "1": {
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          "3": {
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          "5": {
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          "7": {
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            "black": 9,
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            "green": 70,
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          "9": {
            "green": 74,
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            "pink": 61,
            "yellow": 35,
            "other": 139,
            "white": 4,
            "purple": 269,
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            "brown": 29,
            "red": 103,
            "orange": 30,
            "gray": 16,
            "black": 28
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        },
        "V": 0.26126657461721814
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      "sources": [
        "results/23_synesthesia/battery_v2/battery_instruct.json",
        "results/23_synesthesia/battery_v2/analysis.json"
      ],
      "encoding": "Square area = sampled color word share, fixed denominator 1,000; common 30% full-square reference. sRGB fills illustrate color words, not hex outputs.",
      "minimum_width_px": 720,
      "table": "<details class=\"figure-data\"><summary>Text alternative and data</summary><div class=\"table-scroll\" tabindex=\"0\" role=\"region\" aria-label=\"Supporting figure data\"><table><caption>Color-word shares and omitted answer counts</caption><thead><tr><th scope=\"col\">Digit</th><th scope=\"col\">black</th><th scope=\"col\">gray</th><th scope=\"col\">white</th><th scope=\"col\">brown</th><th scope=\"col\">red</th><th scope=\"col\">orange</th><th scope=\"col\">yellow</th><th scope=\"col\">green</th><th scope=\"col\">blue</th><th scope=\"col\">purple</th><th scope=\"col\">pink</th><th scope=\"col\">Other (n)</th><th scope=\"col\">Unparsed (n)</th></tr></thead><tbody><tr><th scope=\"row\">0</th><td>24.9%</td><td>8.3%</td><td>15.2%</td><td>15.3%</td><td>8.1%</td><td>1.9%</td><td>13.9%</td><td>2.2%</td><td>1.7%</td><td>0.8%</td><td>0.4%</td><td>47</td><td>26</td></tr><tr><th scope=\"row\">1</th><td>3.1%</td><td>0.4%</td><td>24.9%</td><td>0.4%</td><td>16.0%</td><td>3.1%</td><td>9.9%</td><td>3.3%</td><td>24.3%</td><td>3.0%</td><td>3.0%</td><td>54</td><td>32</td></tr><tr><th scope=\"row\">2</th><td>0.9%</td><td>0.6%</td><td>1.7%</td><td>2.3%</td><td>12.6%</td><td>11.2%</td><td>25.7%</td><td>22.3%</td><td>6.5%</td><td>4.9%</td><td>2.2%</td><td>53</td><td>38</td></tr><tr><th scope=\"row\">3</th><td>0.5%</td><td>0.4%</td><td>0.4%</td><td>0.8%</td><td>15.2%</td><td>10.8%</td><td>6.1%</td><td>22.2%</td><td>22.6%</td><td>5.7%</td><td>3.2%</td><td>83</td><td>38</td></tr><tr><th scope=\"row\">4</th><td>0.8%</td><td>0.6%</td><td>1.6%</td><td>5.2%</td><td>13.1%</td><td>18.4%</td><td>19.1%</td><td>13.7%</td><td>7.5%</td><td>5.8%</td><td>3.5%</td><td>74</td><td>33</td></tr><tr><th scope=\"row\">5</th><td>0.7%</td><td>3.8%</td><td>0.5%</td><td>6.3%</td><td>7.5%</td><td>10.7%</td><td>7.8%</td><td>15.3%</td><td>7.2%</td><td>16.1%</td><td>5.3%</td><td>128</td><td>60</td></tr><tr><th scope=\"row\">6</th><td>1.5%</td><td>1.7%</td><td>0.4%</td><td>8.1%</td><td>10.5%</td><td>19.8%</td><td>7.7%</td><td>11.8%</td><td>5.2%</td><td>11.3%</td><td>3.0%</td><td>126</td><td>64</td></tr><tr><th scope=\"row\">7</th><td>0.9%</td><td>0.7%</td><td>1.2%</td><td>1.5%</td><td>11.2%</td><td>9.2%</td><td>6.9%</td><td>5.8%</td><td>13.5%</td><td>21.8%</td><td>4.2%</td><td>153</td><td>78</td></tr><tr><th scope=\"row\">8</th><td>24.2%</td><td>1.7%</td><td>5.0%</td><td>9.3%</td><td>9.4%</td><td>9.4%</td><td>8.8%</td><td>7.0%</td><td>4.6%</td><td>5.3%</td><td>1.5%</td><td>95</td><td>43</td></tr><tr><th scope=\"row\">9</th><td>2.8%</td><td>1.6%</td><td>0.4%</td><td>2.9%</td><td>10.3%</td><td>3.0%</td><td>3.5%</td><td>7.4%</td><td>16.0%</td><td>26.9%</td><td>6.1%</td><td>139</td><td>52</td></tr></tbody></table></div></details>"
    },
    "palette-mosaic": {
      "file": "palette-mosaic.svg",
      "sha256": "1ff09c120301197a1df2eb126df0f00e5532e06f543644be2728515ac916bc83",
      "alt": "Ten horizontal bands, one per digit from 0 to 9. Each band contains all 1,000 sampled answers used in the palette grid, one equal-sized tile per answer. Read top to bottom within each column, then left to right. Basic color words use stand-in sRGB colors; off-palette words and unparsed answers are beige. This is not a different model or a neuron-dial condition.",
      "values": {
        "n_runs": 1000,
        "answers": 10000,
        "counts": {
          "0": {
            "purple": 8,
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          "1": {
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          },
          "2": {
            "unparsed": 38,
            "purple": 49,
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            "green": 223,
            "brown": 23,
            "orange": 112,
            "pink": 22,
            "yellow": 257,
            "red": 126,
            "other": 53,
            "blue": 65,
            "black": 9,
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          "3": {
            "red": 152,
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      "sources": [
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        "results/23_synesthesia/battery_v2/analysis.json"
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      "encoding": "One tile per saved sampled answer. Run r is at column r // 10, row r % 10 in its digit band. Equal tile areas; no resampling, sorting, or rounding. Identical categories, counts and 1,000-answer denominator to the palette grid; other and unparsed answers shown in beige.",
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      "table": "<details class=\"figure-data\"><summary>Text alternative and data</summary><div class=\"table-scroll\" tabindex=\"0\" role=\"region\" aria-label=\"Supporting figure data\"><table><caption>Color-word shares and omitted answer counts</caption><thead><tr><th scope=\"col\">Digit</th><th scope=\"col\">black</th><th scope=\"col\">gray</th><th scope=\"col\">white</th><th scope=\"col\">brown</th><th scope=\"col\">red</th><th scope=\"col\">orange</th><th scope=\"col\">yellow</th><th scope=\"col\">green</th><th scope=\"col\">blue</th><th scope=\"col\">purple</th><th scope=\"col\">pink</th><th scope=\"col\">Other (n)</th><th scope=\"col\">Unparsed (n)</th></tr></thead><tbody><tr><th scope=\"row\">0</th><td>24.9%</td><td>8.3%</td><td>15.2%</td><td>15.3%</td><td>8.1%</td><td>1.9%</td><td>13.9%</td><td>2.2%</td><td>1.7%</td><td>0.8%</td><td>0.4%</td><td>47</td><td>26</td></tr><tr><th scope=\"row\">1</th><td>3.1%</td><td>0.4%</td><td>24.9%</td><td>0.4%</td><td>16.0%</td><td>3.1%</td><td>9.9%</td><td>3.3%</td><td>24.3%</td><td>3.0%</td><td>3.0%</td><td>54</td><td>32</td></tr><tr><th scope=\"row\">2</th><td>0.9%</td><td>0.6%</td><td>1.7%</td><td>2.3%</td><td>12.6%</td><td>11.2%</td><td>25.7%</td><td>22.3%</td><td>6.5%</td><td>4.9%</td><td>2.2%</td><td>53</td><td>38</td></tr><tr><th scope=\"row\">3</th><td>0.5%</td><td>0.4%</td><td>0.4%</td><td>0.8%</td><td>15.2%</td><td>10.8%</td><td>6.1%</td><td>22.2%</td><td>22.6%</td><td>5.7%</td><td>3.2%</td><td>83</td><td>38</td></tr><tr><th scope=\"row\">4</th><td>0.8%</td><td>0.6%</td><td>1.6%</td><td>5.2%</td><td>13.1%</td><td>18.4%</td><td>19.1%</td><td>13.7%</td><td>7.5%</td><td>5.8%</td><td>3.5%</td><td>74</td><td>33</td></tr><tr><th scope=\"row\">5</th><td>0.7%</td><td>3.8%</td><td>0.5%</td><td>6.3%</td><td>7.5%</td><td>10.7%</td><td>7.8%</td><td>15.3%</td><td>7.2%</td><td>16.1%</td><td>5.3%</td><td>128</td><td>60</td></tr><tr><th scope=\"row\">6</th><td>1.5%</td><td>1.7%</td><td>0.4%</td><td>8.1%</td><td>10.5%</td><td>19.8%</td><td>7.7%</td><td>11.8%</td><td>5.2%</td><td>11.3%</td><td>3.0%</td><td>126</td><td>64</td></tr><tr><th scope=\"row\">7</th><td>0.9%</td><td>0.7%</td><td>1.2%</td><td>1.5%</td><td>11.2%</td><td>9.2%</td><td>6.9%</td><td>5.8%</td><td>13.5%</td><td>21.8%</td><td>4.2%</td><td>153</td><td>78</td></tr><tr><th scope=\"row\">8</th><td>24.2%</td><td>1.7%</td><td>5.0%</td><td>9.3%</td><td>9.4%</td><td>9.4%</td><td>8.8%</td><td>7.0%</td><td>4.6%</td><td>5.3%</td><td>1.5%</td><td>95</td><td>43</td></tr><tr><th scope=\"row\">9</th><td>2.8%</td><td>1.6%</td><td>0.4%</td><td>2.9%</td><td>10.3%</td><td>3.0%</td><td>3.5%</td><td>7.4%</td><td>16.0%</td><td>26.9%</td><td>6.1%</td><td>139</td><td>52</td></tr></tbody></table></div></details>"
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